Model · Text generation
IFML
A masked diffusion language model adapted from Qwen3.5-4B. The backbone is hybrid: only its attention layers are made bidirectional, and the Gated DeltaNet layers stay causal. This is a base model, with no instruction tuning. Paper: dQwen3.5: Hybrid-Attention Diffusion Language Models. Code: https://github.com/AntonXue/dQwen Needs a CUDA GPU and transformers>=5.13 (tested with torch 2.7.1+cu128, flash-linear-attention 0.5.1). generate decodes the whole canvas at once, committing positions above a confidence threshold (tau=0.9); pass blocklength=32 for left-to-right block decoding, or tau=None, stepsperblock=k for a fixed budget. The 50B-token checkpoint from the paper is…
Open weights
apache-2.0
4.2B parameters
262,144 tokens
transformers
This is an uncensored version of TokenRhythm/NeoHorse-1-4B created with abliteration (see remove-refusals-with-transformers to know more about it). This is a crude, proof-of-concept implementation to remove refusals from an LLM model without using TransformerLens. Layers 5-17 are being ablated (0-based indexing). The MTP and Visual components were extracted from the original Qwen/Qwen3.5-4B and can provide excellent support. If needed, you only need to copy the contents of MTP-Visual to overwrite the model directory. You can use this model in your applications by loading it with Hugging Face's transformers library: - Risk of Sensitive or Controversial Outputs: This model’s safety filtering…
Open weights
apache-2.0
4.2B parameters
262,144 tokens
transformers
FP8 quantization of alibiserikbay/JevK5, published by Liodon AI. Quantized with llm-compressor using the FP8DYNAMIC scheme: weights are cast to FP8 (E4M3) per-channel ahead of time, activations are quantized to FP8 dynamically per-token at inference time. No calibration dataset is needed for this scheme, so the quantized weights are numerically just a direct cast of the original — no calibration-set bias to worry about. lmhead is left unquantized (standard practice — negligible size, disproportionate quality impact if quantized). vLLM Text Generation Inference (TGI) SGLang FP8 execution requires an NVIDIA GPU with compute capability ≥ 8.9 (Ada/Hopper/Blackwell — RTX 40-series, L4/L40S…
Open weights
other
4.2B parameters
262,144 tokens
transformers
This is a text-only, 4-bit affine/group-64 MLX derivative for Minstrel's optional Advanced local language engine. It is based on the pinned mlx-community/Qwen3.5-4B-4bit snapshot 0e7ffd5c629ef7719d4cbc04069232580bfa9d9c. The deriveqwen35text.py tool removed vision tensors and capped the packaged context at 8,192 tokens. The signed Minstrel manifest and provenance file are included. The manifest is for Minstrel's detailed-local slot, artifact version 0.1.0; it is not a general quality certification. The reference upstream is Qwen/Qwen3.5-4B at 851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a. The source conversion does not establish the exact Qwen revision it used. The Apache-2.0 license file is…
Open weights
apache-2.0
4.2B parameters
8,192 tokens
mlx
Qwen3-8B after full-parameter supervised fine-tuning for mathematical keyword and meaning generation. This is the exact saved epoch 4 checkpoint at optimizer step 2976. The repository contains the model weights, configuration, and tokenizer files for inference. Training dataset: devpotatopotato/math-keyword-training, keyword-261001-bigmath-gpt-6-sol.jsonl. Use the tokenizer chat template with enablethinking=False. Pass the following template as a user message and replace {problem} with the problem text
Open weights
apache-2.0
4.1B parameters
40,960 tokens
transformers
Model · Text generation
Qwen
Qwen3 is the latest generation of large language models in Qwen series, offering a comprehensive suite of dense and mixture-of-experts (MoE) models. Built upon extensive training, Qwen3 delivers groundbreaking advancements in reasoning, instruction-following, agent capabilities, and multilingual support, with the following key features: - Uniquely support of seamless switching between thinking mode (for complex logical reasoning, math, and coding) and non-thinking mode (for efficient, general-purpose dialogue) within single model, ensuring optimal performance across various scenarios. - Significantly enhancement in its reasoning capabilities, surpassing previous QwQ (in thinking mode) and…
Open weights
apache-2.0
4B parameters
40,960 tokens
transformers
